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  • Publication in Economics
  • A.I. and the new definitons of work
		The Paradigm Change on Social-economic two way of Interaction 
	   

Status: First notes...

How definition of work in economics relates to definition of work in physics. How current advances in technology, in particular health related technology can be of use on gathering live data from work activities. Artificial Intelligence (AI) is a rapidly advancing technology with the potential to drastically reshape US employment [3][4]. Unlike previous technologies, examples of AI have applications in a variety of highly educated, well-paid, and predominantly urban industries (3), including medicine[6][7], finance[8], and information technology[9]. With AI’s potential to change the nature of work, how can policy makers facilitate the next generation of employment opportunities? Studying this question is made difficult by the complexity of economic systems and AI’s differential impact on different types of labor.

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you can read the very first doodle note on this subject on my Facebook articles page.


 

  • Scientific Publication
  • Self Sensing Materials
		
		    Temperature Dependence of Self-Sensing Micro Carbon Polymer-based Composites for Further Development as IoT Sensor Device
		
	   

status: Submitted for Peer Review...

authors: Miguel Tomás, Alexandre Silva de Vargas, Said Jalali

Abstract
This article investigates the dependency of temperature in electrical resistance (R) change on micro carbon fiber polymer composites (MCFPC), for further development as an IoT sensor. Composites at test were submitted to temperature loading, with a constant strain of 0.0%, for assessment of R when a change in composite’s temperature occurs. It was observed composite’s response to follow an Arrhenius function, for temperatures ranging from -10°C to 40°C and carbon fiber content ranging from 13% to 50% (wt%). The apparent activation energy was calculated to evaluate further differences between carbon fiber contents and the sensitivity factor, S_T, due to temperature is determined. Specimens were also put to test with a constant strain of 2,86% for assessment of creep. A good agreement was found between creep and R over the period of time in analysis. The discrete latent variable model was found to best fit results. The sensitivity factor changes in regard to stress relaxation, S_R, is determined. The properties of MCFPC investigated here can be used to establish relationships between electrical resistance outputs and environmental loading conditions on this type of composites, enabling the possibility of deployment as part of a management system network for structural monitoring with real-time data acquisition.

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  • Scientific Publication
  • Time Dependency on Modelling Difussion in Concrete
		Analysis of the Time Dependency of Chloride Diffusion on Modelling Concrete Durability
	   

Status: First notes...

authors: Miguel Tomás

Abstract
One of the main concerns in modeling durability has to do with chloride-induced corrosion and the main cause of deterioration of reinforced concrete structures. To assess chloride penetration in concrete is required to determine with a fair amount of precision the diffusion coefficient of chloride ion, which governs the mechanism of penetration in concrete. However, is difficult to obtain chloride diffusion coefficients from experiments due to time and cost limitations. In regards to modeling, to better understand time dependency in chloride diffusion, current developments are analyzed. Furthermore, a novel approach using Einstein’s Brownian motion is also presented that enables a macroscopic analysis of chloride diffusion in concrete medium but also allows scale modeling and support for the redefinition of diffusion over extended periods of time. To better understand the discussed models, an experimental campaign was carried out. Concrete samples were made with ordinary Portland cement (OPC) with four different w/b ratios ranging from 0.40 to 0.78. Laboratory testing consisted of the rapid migration test with different testing times aimed at the evaluation of ionic movement of chlorides due to applied electric field during testing and how it affects the latter. The results presented are in agreement with the proposed model approach through a comparison model analysis.

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  • Scientific Publication
  • Machine Learning Modelling
		Calibration Automation of ER Measurements on Self-Sensing Carbon Fiber Composites for Edge Computing Monitoring Solutions
	   

Status: modelling data on Google Colab ...

authors: Miguel Tomás

Abstract
Self-sensing ability of materials, in particular carbon fiber composites, is a must have factor or remote assessing the serviceability of a particular structural composite element. When investigating the sensing abilities of carbon fiber composites is frequently found electrical resistance change measurements to vary from its correlated mechanical properties, namely stress and strain. One of the main challenges when making a carbon fiber self-sensing material (CFSSM) is how to attain reproducibility of the sensing ability. This article investigates the dependency of temperature in electrical resistance (R) change when specimens are submitted to a constant strain external loading of 2,86% for assessment of creep. The Bayesian regularization backpropagation algorithm is built to predict the electrical resistance of the CFSSM specimens based on 1500 data collected from experiments. performance of the model is evaluated by three specific statistical criteria, such as the Pearson correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). results show that the proposed algorithm performed well for the prediction of the composite's electrical resistance output. It can be concluded that the backpropagation neural network-based machine learning improves accuracy and automates further the development to deployment phases of this type of materials on site for remote live monitoring part of a management system network for structural monitoring with real-time data acquisition.

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  • Scientific Thesis Publication
  • Neuromorphic AI & Edge Computing
		Neuromorphic Neural Networks for Image Feature Extraction on Fog Computing Environments
	   

Status: writing happening right now as you read this...

authors: Miguel Tomás

Abstract
Deep learning using backpropagation has achieved success across many domains on solving real-world problems with high-performance results, however, when run on traditional computer systems these models are very expensive in terms of power consumption. Recently, it has been developed a novel approach to computer systems for the processing of neural network algorithms called neuromorphic computing. This type of computing is a step forward towards mimicking the biological brain by enabling spiking neural networks and unprecedented energy efficiency when comparing the accuracy of results. This work follows works published elsewhere on this subject and proposes a new model approach for the development of a deep neural network using an asynchronous data exchange network and time-independent clusters of neural networks to form one single global deep neural network suitable to be implemented on neuromorphic computing technologies.

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  • Scientific Publication
  • Transfer Learning & Machine Learning
		Real-Time Logistics Supported with Transfer Learning at a Small Start-Up Enterprise in the Construction Sector
	   

status: Submitted for Peer Review...

authors: Miguel Tomás

Abstract
Logistics with real-time management of information on all related to construction works, including building materials and management of human resources, is a novelty, for micro and small enterprises starting up in the construction sector. Enterprises this size are being run using traditional turn-of-the-century technology tools, namely excel spreadsheets as data storage. This paper presents a case study of technology implementation at a startup enterprise in the Belgian construction sector between 2018 and mid-2020 and discusses the challenges the enterprise had to overcome during the implementation process. A logistics platform was developed during that period as part of the digital transformation. This enabled the enterprise to move forward and improve its work methodologies to work with real-time data. Results after 18 months demonstrated an increased efficiency on all work processes, with reduction of losses on all aspects of work activities and improved control on production estimates and measurements.

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